Overview

Dataset statistics

Number of variables13
Number of observations359
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.2 KiB
Average record size in memory166.1 B

Variable types

Numeric11
Categorical2

Alerts

Word has a high cardinality: 359 distinct valuesHigh cardinality
Contest number is highly overall correlated with Number of reported results and 2 other fieldsHigh correlation
Number of reported results is highly overall correlated with Contest number and 2 other fieldsHigh correlation
Number in hard mode is highly overall correlated with Contest number and 2 other fieldsHigh correlation
1 try is highly overall correlated with 2 triesHigh correlation
2 tries is highly overall correlated with 1 try and 3 other fieldsHigh correlation
3 tries is highly overall correlated with 2 tries and 3 other fieldsHigh correlation
4 tries is highly overall correlated with 6 tries and 1 other fieldsHigh correlation
5 tries is highly overall correlated with 2 tries and 3 other fieldsHigh correlation
6 tries is highly overall correlated with 2 tries and 4 other fieldsHigh correlation
7 or more tries is highly overall correlated with 3 tries and 3 other fieldsHigh correlation
Hard Mode Percentage is highly overall correlated with Contest number and 2 other fieldsHigh correlation
Contest number is uniformly distributedUniform
Word is uniformly distributedUniform
Contest number has unique valuesUnique
Word has unique valuesUnique
Hard Mode Percentage has unique valuesUnique
1 try has 221 (61.6%) zerosZeros
7 or more tries has 27 (7.5%) zerosZeros

Reproduction

Analysis started2023-02-18 17:33:22.892521
Analysis finished2023-02-18 17:33:40.481612
Duration17.59 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Contest number
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct359
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean381
Minimum202
Maximum560
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-18T12:33:40.601929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum202
5-th percentile219.9
Q1291.5
median381
Q3470.5
95-th percentile542.1
Maximum560
Range358
Interquartile range (IQR)179

Descriptive statistics

Standard deviation103.77861
Coefficient of variation (CV)0.2723848
Kurtosis-1.2
Mean381
Median Absolute Deviation (MAD)90
Skewness0
Sum136779
Variance10770
MonotonicityStrictly decreasing
2023-02-18T12:33:40.720095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
560 1
 
0.3%
335 1
 
0.3%
315 1
 
0.3%
316 1
 
0.3%
317 1
 
0.3%
318 1
 
0.3%
319 1
 
0.3%
320 1
 
0.3%
321 1
 
0.3%
322 1
 
0.3%
Other values (349) 349
97.2%
ValueCountFrequency (%)
202 1
0.3%
203 1
0.3%
204 1
0.3%
205 1
0.3%
206 1
0.3%
207 1
0.3%
208 1
0.3%
209 1
0.3%
210 1
0.3%
211 1
0.3%
ValueCountFrequency (%)
560 1
0.3%
559 1
0.3%
558 1
0.3%
557 1
0.3%
556 1
0.3%
555 1
0.3%
554 1
0.3%
553 1
0.3%
552 1
0.3%
551 1
0.3%

Word
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct359
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size24.6 KiB
manly
 
1
gamer
 
1
larva
 
1
forgo
 
1
story
 
1
Other values (354)
354 

Length

Max length6
Median length5
Mean length5
Min length4

Characters and Unicode

Total characters1795
Distinct characters28
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique359 ?
Unique (%)100.0%

Sample

1st rowmanly
2nd rowmolar
3rd rowhavoc
4th rowimpel
5th rowcondo

Common Values

ValueCountFrequency (%)
manly 1
 
0.3%
gamer 1
 
0.3%
larva 1
 
0.3%
forgo 1
 
0.3%
story 1
 
0.3%
hairy 1
 
0.3%
train 1
 
0.3%
homer 1
 
0.3%
badge 1
 
0.3%
midst 1
 
0.3%
Other values (349) 349
97.2%

Length

2023-02-18T12:33:40.871782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
manly 1
 
0.3%
sting 1
 
0.3%
havoc 1
 
0.3%
impel 1
 
0.3%
condo 1
 
0.3%
judge 1
 
0.3%
extra 1
 
0.3%
poise 1
 
0.3%
aorta 1
 
0.3%
excel 1
 
0.3%
Other values (349) 349
97.2%

Most occurring characters

ValueCountFrequency (%)
e 184
 
10.3%
a 158
 
8.8%
r 134
 
7.5%
o 133
 
7.4%
t 130
 
7.2%
l 112
 
6.2%
i 101
 
5.6%
n 88
 
4.9%
s 87
 
4.8%
c 71
 
4.0%
Other values (18) 597
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1794
99.9%
Space Separator 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 184
 
10.3%
a 158
 
8.8%
r 134
 
7.5%
o 133
 
7.4%
t 130
 
7.2%
l 112
 
6.2%
i 101
 
5.6%
n 88
 
4.9%
s 87
 
4.8%
c 71
 
4.0%
Other values (17) 596
33.2%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1794
99.9%
Common 1
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 184
 
10.3%
a 158
 
8.8%
r 134
 
7.5%
o 133
 
7.4%
t 130
 
7.2%
l 112
 
6.2%
i 101
 
5.6%
n 88
 
4.9%
s 87
 
4.8%
c 71
 
4.0%
Other values (17) 596
33.2%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1794
99.9%
None 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 184
 
10.3%
a 158
 
8.8%
r 134
 
7.5%
o 133
 
7.4%
t 130
 
7.2%
l 112
 
6.2%
i 101
 
5.6%
n 88
 
4.9%
s 87
 
4.8%
c 71
 
4.0%
Other values (17) 596
33.2%
None
ValueCountFrequency (%)
ï 1
100.0%
Distinct357
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90918.877
Minimum2569
Maximum361908
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-18T12:33:40.997728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2569
5-th percentile22320.4
Q130308.5
median44578
Q3120294
95-th percentile288377.3
Maximum361908
Range359339
Interquartile range (IQR)89985.5

Descriptive statistics

Standard deviation89274.375
Coefficient of variation (CV)0.98191242
Kurtosis0.89282221
Mean90918.877
Median Absolute Deviation (MAD)19002
Skewness1.4474822
Sum32639877
Variance7.969914 × 109
MonotonicityNot monotonic
2023-02-18T12:33:41.125128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
218595 2
 
0.6%
36223 2
 
0.6%
20380 1
 
0.3%
88932 1
 
0.3%
95643 1
 
0.3%
85817 1
 
0.3%
107750 1
 
0.3%
85979 1
 
0.3%
76292 1
 
0.3%
74458 1
 
0.3%
Other values (347) 347
96.7%
ValueCountFrequency (%)
2569 1
0.3%
15554 1
0.3%
20001 1
0.3%
20011 1
0.3%
20160 1
0.3%
20281 1
0.3%
20380 1
0.3%
20490 1
0.3%
20824 1
0.3%
20879 1
0.3%
ValueCountFrequency (%)
361908 1
0.3%
359679 1
0.3%
358176 1
0.3%
351663 1
0.3%
342003 1
0.3%
341314 1
0.3%
336236 1
0.3%
331844 1
0.3%
319698 1
0.3%
313220 1
0.3%

Number in hard mode
Real number (ℝ)

Distinct344
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5098.351
Minimum1362
Maximum15369
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-18T12:33:41.247238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1362
5-th percentile2106
Q12848.5
median3548
Q37004.5
95-th percentile11415
Maximum15369
Range14007
Interquartile range (IQR)4156

Descriptive statistics

Standard deviation3166.6124
Coefficient of variation (CV)0.62110522
Kurtosis0.47615552
Mean5098.351
Median Absolute Deviation (MAD)1126
Skewness1.2117147
Sum1830308
Variance10027434
MonotonicityNot monotonic
2023-02-18T12:33:41.365119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10343 2
 
0.6%
3249 2
 
0.6%
2356 2
 
0.6%
2200 2
 
0.6%
3123 2
 
0.6%
2752 2
 
0.6%
2677 2
 
0.6%
3345 2
 
0.6%
2987 2
 
0.6%
2261 2
 
0.6%
Other values (334) 339
94.4%
ValueCountFrequency (%)
1362 1
0.3%
1562 1
0.3%
1763 1
0.3%
1863 1
0.3%
1899 1
0.3%
1911 1
0.3%
1913 1
0.3%
1919 1
0.3%
1937 1
0.3%
1973 1
0.3%
ValueCountFrequency (%)
15369 1
0.3%
14813 1
0.3%
14609 1
0.3%
14205 1
0.3%
13846 1
0.3%
13716 1
0.3%
13708 1
0.3%
13606 1
0.3%
13480 1
0.3%
13347 1
0.3%

1 try
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0047075209
Minimum0
Maximum0.06
Zeros221
Zeros (%)61.6%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-18T12:33:41.486326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.01
95-th percentile0.01
Maximum0.06
Range0.06
Interquartile range (IQR)0.01

Descriptive statistics

Standard deviation0.0078292336
Coefficient of variation (CV)1.6631331
Kurtosis18.615735
Mean0.0047075209
Median Absolute Deviation (MAD)0
Skewness3.4160919
Sum1.69
Variance6.1296899 × 10-5
MonotonicityNot monotonic
2023-02-18T12:33:41.581776image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 221
61.6%
0.01 123
34.3%
0.02 9
 
2.5%
0.06 2
 
0.6%
0.05 2
 
0.6%
0.03 2
 
0.6%
ValueCountFrequency (%)
0 221
61.6%
0.01 123
34.3%
0.02 9
 
2.5%
0.03 2
 
0.6%
0.05 2
 
0.6%
0.06 2
 
0.6%
ValueCountFrequency (%)
0.06 2
 
0.6%
0.05 2
 
0.6%
0.03 2
 
0.6%
0.02 9
 
2.5%
0.01 123
34.3%
0 221
61.6%

2 tries
Real number (ℝ)

Distinct22
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.058440111
Minimum0
Maximum0.26
Zeros3
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-18T12:33:41.679349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.03
median0.05
Q30.07
95-th percentile0.14
Maximum0.26
Range0.26
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.040765252
Coefficient of variation (CV)0.69755603
Kurtosis3.0973797
Mean0.058440111
Median Absolute Deviation (MAD)0.02
Skewness1.5541354
Sum20.98
Variance0.0016618058
MonotonicityNot monotonic
2023-02-18T12:33:41.785437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.02 56
15.6%
0.06 45
12.5%
0.04 45
12.5%
0.05 43
12.0%
0.03 38
10.6%
0.07 24
6.7%
0.08 23
6.4%
0.01 16
 
4.5%
0.1 14
 
3.9%
0.09 13
 
3.6%
Other values (12) 42
11.7%
ValueCountFrequency (%)
0 3
 
0.8%
0.01 16
 
4.5%
0.02 56
15.6%
0.03 38
10.6%
0.04 45
12.5%
0.05 43
12.0%
0.06 45
12.5%
0.07 24
6.7%
0.08 23
6.4%
0.09 13
 
3.6%
ValueCountFrequency (%)
0.26 1
 
0.3%
0.22 1
 
0.3%
0.21 1
 
0.3%
0.19 3
 
0.8%
0.18 1
 
0.3%
0.17 3
 
0.8%
0.16 5
1.4%
0.14 9
2.5%
0.13 5
1.4%
0.12 6
1.7%

3 tries
Real number (ℝ)

Distinct36
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22727019
Minimum0.04
Maximum0.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-18T12:33:41.891730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile0.1
Q10.17
median0.23
Q30.29
95-th percentile0.35
Maximum0.47
Range0.43
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.077810915
Coefficient of variation (CV)0.34237184
Kurtosis-0.49224944
Mean0.22727019
Median Absolute Deviation (MAD)0.06
Skewness-0.011042003
Sum81.59
Variance0.0060545385
MonotonicityNot monotonic
2023-02-18T12:33:42.004107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0.16 19
 
5.3%
0.17 18
 
5.0%
0.26 18
 
5.0%
0.22 18
 
5.0%
0.23 17
 
4.7%
0.28 16
 
4.5%
0.25 16
 
4.5%
0.3 16
 
4.5%
0.29 16
 
4.5%
0.19 16
 
4.5%
Other values (26) 189
52.6%
ValueCountFrequency (%)
0.04 4
 
1.1%
0.05 1
 
0.3%
0.07 1
 
0.3%
0.08 2
 
0.6%
0.09 6
1.7%
0.1 6
1.7%
0.11 10
2.8%
0.12 5
 
1.4%
0.13 13
3.6%
0.14 10
2.8%
ValueCountFrequency (%)
0.47 1
 
0.3%
0.39 2
 
0.6%
0.38 5
 
1.4%
0.37 3
 
0.8%
0.36 6
 
1.7%
0.35 6
 
1.7%
0.34 7
1.9%
0.33 6
 
1.7%
0.32 15
4.2%
0.31 11
3.1%

4 tries
Real number (ℝ)

Distinct32
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32927577
Minimum0.11
Maximum0.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-18T12:33:42.112189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.11
5-th percentile0.24
Q10.3
median0.34
Q30.36
95-th percentile0.401
Maximum0.49
Range0.38
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.053539827
Coefficient of variation (CV)0.16259875
Kurtosis1.1520793
Mean0.32927577
Median Absolute Deviation (MAD)0.03
Skewness-0.63689703
Sum118.21
Variance0.0028665131
MonotonicityNot monotonic
2023-02-18T12:33:42.219537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.35 38
 
10.6%
0.33 32
 
8.9%
0.34 32
 
8.9%
0.38 27
 
7.5%
0.32 27
 
7.5%
0.36 25
 
7.0%
0.3 20
 
5.6%
0.29 18
 
5.0%
0.27 17
 
4.7%
0.31 16
 
4.5%
Other values (22) 107
29.8%
ValueCountFrequency (%)
0.11 1
 
0.3%
0.14 1
 
0.3%
0.16 1
 
0.3%
0.17 1
 
0.3%
0.18 1
 
0.3%
0.19 2
 
0.6%
0.2 2
 
0.6%
0.22 5
1.4%
0.23 3
 
0.8%
0.24 8
2.2%
ValueCountFrequency (%)
0.49 1
 
0.3%
0.47 1
 
0.3%
0.44 2
 
0.6%
0.43 3
 
0.8%
0.42 2
 
0.6%
0.41 9
 
2.5%
0.4 11
3.1%
0.39 16
4.5%
0.38 27
7.5%
0.37 14
3.9%

5 tries
Real number (ℝ)

Distinct31
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23637883
Minimum0.09
Maximum0.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-18T12:33:42.358822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.09
5-th percentile0.14
Q10.19
median0.24
Q30.28
95-th percentile0.33
Maximum0.44
Range0.35
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation0.059469077
Coefficient of variation (CV)0.25158377
Kurtosis-0.25524851
Mean0.23637883
Median Absolute Deviation (MAD)0.04
Skewness0.06543342
Sum84.86
Variance0.0035365712
MonotonicityNot monotonic
2023-02-18T12:33:42.471362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.24 32
 
8.9%
0.21 26
 
7.2%
0.27 25
 
7.0%
0.19 22
 
6.1%
0.28 20
 
5.6%
0.26 20
 
5.6%
0.29 19
 
5.3%
0.22 19
 
5.3%
0.25 17
 
4.7%
0.2 17
 
4.7%
Other values (21) 142
39.6%
ValueCountFrequency (%)
0.09 1
 
0.3%
0.1 2
 
0.6%
0.11 2
 
0.6%
0.12 4
 
1.1%
0.13 8
2.2%
0.14 6
 
1.7%
0.15 12
3.3%
0.16 10
2.8%
0.17 10
2.8%
0.18 16
4.5%
ValueCountFrequency (%)
0.44 1
 
0.3%
0.38 1
 
0.3%
0.37 2
 
0.6%
0.36 4
 
1.1%
0.35 3
 
0.8%
0.34 4
 
1.1%
0.33 11
3.1%
0.32 10
2.8%
0.31 11
3.1%
0.3 9
2.5%

6 tries
Real number (ℝ)

Distinct30
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11559889
Minimum0.02
Maximum0.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-18T12:33:42.597091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.04
Q10.07
median0.1
Q30.15
95-th percentile0.23
Maximum0.37
Range0.35
Interquartile range (IQR)0.08

Descriptive statistics

Standard deviation0.062074164
Coefficient of variation (CV)0.53697891
Kurtosis0.97535851
Mean0.11559889
Median Absolute Deviation (MAD)0.04
Skewness1.0296294
Sum41.5
Variance0.0038532018
MonotonicityNot monotonic
2023-02-18T12:33:42.717958image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.09 30
 
8.4%
0.06 30
 
8.4%
0.08 29
 
8.1%
0.07 25
 
7.0%
0.1 25
 
7.0%
0.05 24
 
6.7%
0.12 22
 
6.1%
0.13 19
 
5.3%
0.11 18
 
5.0%
0.04 15
 
4.2%
Other values (20) 122
34.0%
ValueCountFrequency (%)
0.02 1
 
0.3%
0.03 11
 
3.1%
0.04 15
4.2%
0.05 24
6.7%
0.06 30
8.4%
0.07 25
7.0%
0.08 29
8.1%
0.09 30
8.4%
0.1 25
7.0%
0.11 18
5.0%
ValueCountFrequency (%)
0.37 1
 
0.3%
0.35 1
 
0.3%
0.33 1
 
0.3%
0.3 1
 
0.3%
0.28 1
 
0.3%
0.26 4
1.1%
0.25 3
 
0.8%
0.24 5
1.4%
0.23 8
2.2%
0.22 7
1.9%

7 or more tries
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.028050139
Minimum0
Maximum0.48
Zeros27
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-18T12:33:42.822864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01
median0.02
Q30.03
95-th percentile0.1
Maximum0.48
Range0.48
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.041218694
Coefficient of variation (CV)1.4694649
Kurtosis45.491869
Mean0.028050139
Median Absolute Deviation (MAD)0.01
Skewness5.4610988
Sum10.07
Variance0.0016989807
MonotonicityNot monotonic
2023-02-18T12:33:42.932789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.01 146
40.7%
0.02 75
20.9%
0.03 39
 
10.9%
0 27
 
7.5%
0.04 23
 
6.4%
0.05 11
 
3.1%
0.07 7
 
1.9%
0.06 5
 
1.4%
0.08 4
 
1.1%
0.09 3
 
0.8%
Other values (11) 19
 
5.3%
ValueCountFrequency (%)
0 27
 
7.5%
0.01 146
40.7%
0.02 75
20.9%
0.03 39
 
10.9%
0.04 23
 
6.4%
0.05 11
 
3.1%
0.06 5
 
1.4%
0.07 7
 
1.9%
0.08 4
 
1.1%
0.09 3
 
0.8%
ValueCountFrequency (%)
0.48 1
 
0.3%
0.26 1
 
0.3%
0.23 1
 
0.3%
0.2 1
 
0.3%
0.18 1
 
0.3%
0.15 3
0.8%
0.14 2
0.6%
0.13 2
0.6%
0.12 1
 
0.3%
0.11 3
0.8%

Hard Mode Percentage
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct359
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7634314
Minimum1.1709332
Maximum93.616193
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-18T12:33:43.054982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.1709332
5-th percentile3.1190716
Q16.3516783
median8.3513823
Q39.2987531
95-th percentile9.8364925
Maximum93.616193
Range92.44526
Interquartile range (IQR)2.9470748

Descriptive statistics

Standard deviation5.0586535
Coefficient of variation (CV)0.6516002
Kurtosis232.75244
Mean7.7634314
Median Absolute Deviation (MAD)1.1586781
Skewness13.628864
Sum2787.0719
Variance25.589975
MonotonicityNot monotonic
2023-02-18T12:33:43.188095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.317958783 1
 
0.3%
7.495277889 1
 
0.3%
7.371363362 1
 
0.3%
7.338587138 1
 
0.3%
6.827472999 1
 
0.3%
6.92287076 1
 
0.3%
6.722041763 1
 
0.3%
7.342490608 1
 
0.3%
7.185550254 1
 
0.3%
7.028123237 1
 
0.3%
Other values (349) 349
97.2%
ValueCountFrequency (%)
1.170933179 1
0.3%
1.689197569 1
0.3%
1.736894476 1
0.3%
1.960618664 1
0.3%
2.091236048 1
0.3%
2.09270633 1
0.3%
2.233512131 1
0.3%
2.26102584 1
0.3%
2.351254396 1
0.3%
2.363925213 1
0.3%
ValueCountFrequency (%)
93.61619307 1
0.3%
13.33357574 1
0.3%
11.06971508 1
0.3%
10.31797534 1
0.3%
10.27201263 1
0.3%
10.20938484 1
0.3%
10.11673152 1
0.3%
10.10943021 1
0.3%
10.08109397 1
0.3%
10.04243281 1
0.3%

Cluster
Categorical

Distinct3
Distinct (%)0.8%
Missing1
Missing (%)0.3%
Memory size23.8 KiB
2.0
166 
3.0
98 
1.0
94 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1074
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 166
46.2%
3.0 98
27.3%
1.0 94
26.2%
(Missing) 1
 
0.3%

Length

2023-02-18T12:33:43.538382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-18T12:33:43.666621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2.0 166
46.4%
3.0 98
27.4%
1.0 94
26.3%

Most occurring characters

ValueCountFrequency (%)
. 358
33.3%
0 358
33.3%
2 166
15.5%
3 98
 
9.1%
1 94
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 716
66.7%
Other Punctuation 358
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 358
50.0%
2 166
23.2%
3 98
 
13.7%
1 94
 
13.1%
Other Punctuation
ValueCountFrequency (%)
. 358
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1074
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 358
33.3%
0 358
33.3%
2 166
15.5%
3 98
 
9.1%
1 94
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 358
33.3%
0 358
33.3%
2 166
15.5%
3 98
 
9.1%
1 94
 
8.8%

Interactions

2023-02-18T12:33:38.662455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:23.708441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:25.229471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:26.717306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:28.291199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:29.691049image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:31.056584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:32.525239image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:33.994389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:35.531840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:36.932815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:38.802853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:23.879160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:25.370656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:26.862316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:28.425412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:29.813171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:31.188543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:32.667345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:34.127590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:35.662906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:37.052553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:38.936999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:24.019245image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:25.511051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:27.120609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:28.559435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:29.941514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:31.330146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:32.799431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:34.273824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:35.806467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:37.190043image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:39.066565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:24.165120image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:25.642064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:27.259421image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:28.685536image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:30.076144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:31.452617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:32.940491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:34.407815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:35.939639image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:37.325363image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:39.182154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:24.296099image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:25.780841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:27.395601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:28.803317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:30.191658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:31.572776image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:33.077474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:34.541583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:36.058013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:37.488166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:39.312951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:24.423975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:25.919184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:27.515320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:28.924841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:30.320816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:31.695771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:33.218415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:34.667301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:36.187356image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:37.641389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:39.425975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:24.530354image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:26.039693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:27.629965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:29.039489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:30.433594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:31.802874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:33.341523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:34.781957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:36.305580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:37.761553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:39.575767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:24.673813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:26.179639image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:27.766733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:29.181451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:30.572184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:32.055253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:33.480029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:34.914212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:36.439256image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:37.898509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:39.708259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:24.822637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:26.325687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:27.906820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:29.314672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:30.699024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:32.186909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:33.624457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:35.058918image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:36.564943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:38.200401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:39.834878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:24.963414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:26.475553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:28.048949image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:29.462532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:30.815153image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:32.304643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:33.753091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:35.246779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:36.686690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:38.363826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:39.966212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:25.090930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:26.599537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:28.173089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:29.578240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:30.936268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:32.417906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:33.879247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:35.392877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:36.814371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-02-18T12:33:38.530999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2023-02-18T12:33:43.777319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Contest numberNumber of reported resultsNumber in hard mode1 try2 tries3 tries4 tries5 tries6 tries7 or more triesHard Mode PercentageCluster
Contest number1.000-0.983-0.884-0.426-0.0320.0680.239-0.057-0.185-0.1680.9630.144
Number of reported results-0.9831.0000.9430.4580.094-0.019-0.255-0.0100.1440.150-0.9550.167
Number in hard mode-0.8840.9431.0000.3760.084-0.040-0.260-0.0120.1570.177-0.8400.115
1 try-0.4260.4580.3761.0000.5400.364-0.341-0.422-0.225-0.118-0.4770.126
2 tries-0.0320.0940.0840.5401.0000.843-0.116-0.844-0.675-0.497-0.1260.000
3 tries0.068-0.019-0.0400.3640.8431.0000.264-0.907-0.912-0.756-0.0430.054
4 tries0.239-0.255-0.260-0.341-0.1160.2641.000-0.051-0.507-0.6160.1840.027
5 tries-0.057-0.010-0.012-0.422-0.844-0.907-0.0511.0000.7810.5550.0240.120
6 tries-0.1850.1440.157-0.225-0.675-0.912-0.5070.7811.0000.906-0.0720.113
7 or more tries-0.1680.1500.177-0.118-0.497-0.756-0.6160.5550.9061.000-0.0580.045
Hard Mode\nPercentage0.963-0.955-0.840-0.477-0.126-0.0430.1840.024-0.072-0.0581.0000.000
Cluster0.1440.1670.1150.1260.0000.0540.0270.1200.1130.0450.0001.000

Missing values

2023-02-18T12:33:40.153063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-18T12:33:40.369675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Contest numberWordNumber of reported resultsNumber in hard mode1 try2 tries3 tries4 tries5 tries6 tries7 or more triesHard Mode PercentageCluster
Date_c
2022-12-31560manly2038018990.000.020.170.370.290.120.029.3179592.0
2022-12-30559molar2120419730.000.040.210.380.260.090.019.3048482.0
2022-12-29558havoc2000119190.000.020.160.380.300.120.029.5945202.0
2022-12-28557impel2016019370.000.030.210.400.250.090.019.6081352.0
2022-12-27556condo2087920120.000.020.170.350.290.140.039.6364772.0
2022-12-26555judge2001120430.000.020.080.160.260.330.1410.2093851.0
2022-12-25554extra1555415620.010.050.200.350.280.100.0110.0424332.0
2022-12-24553poise2028119110.020.110.340.320.150.060.019.4226123.0
2022-12-23552aorta2193721120.000.070.260.350.200.100.039.6275702.0
2022-12-22551excel2049020340.000.010.130.340.340.150.029.9267941.0
Contest numberWordNumber of reported resultsNumber in hard mode1 try2 tries3 tries4 tries5 tries6 tries7 or more triesHard Mode PercentageCluster
Date_c
2022-01-16211solar20960949550.010.090.320.320.180.070.012.3639253.0
2022-01-15210panic20588046550.010.090.350.340.160.050.012.2610261.0
2022-01-14209tangy16948439850.010.040.210.300.240.150.052.3512541.0
2022-01-13208abbey13272633450.010.020.130.290.310.200.032.5202301.0
2022-01-12207favor13758630730.010.040.150.260.290.210.042.2335123.0
2022-01-11206drink15388030170.010.090.350.340.160.050.011.9606191.0
2022-01-10205query10713422420.010.040.160.300.300.170.022.0927061.0
2022-01-09204gorge9147719130.010.030.130.270.300.220.042.0912362.0
2022-01-08203crank10150317630.010.050.230.310.240.140.021.7368942.0
2022-01-07202slump8063013620.010.030.230.390.240.090.011.689198NaN